Arvind Narayanan
policy— at Princeton University
USA
AI Policy / Privacy. Princeton University.
24 papers found
Promises and pitfalls of artificial intelligence for legal applications
arXiv (Cornell University)20247 citations
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
arXiv (Cornell University)20244 citations
AI Risk Management Should Incorporate Both Safety and Security
arXiv (Cornell University)20241 citations
Inference Scaling fLaws: The Limits of LLM Resampling with Imperfect Verifiers
arXiv (Cornell University)20241 citations
A Safe Harbor for AI Evaluation and Red Teaming
arXiv (Cornell University)20248 citations
Foundation Model Transparency Reports
Proceedings of the AAAI/ACM Conference on AI Ethics and Society202426 citations
How large language models can reshape collective intelligence
Nature Human Behaviour202464 citations
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
Science Advances202457 citations
Promises and Pitfalls of Artificial Intelligence for Legal Applications
SSRN Electronic Journal202418 citations
CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark
arXiv (Cornell University)20243 citations
The limitations of machine learning models for predicting scientific replicability
Proceedings of the National Academy of Sciences20238 citations
Leakage and the reproducibility crisis in machine-learning-based science
Patterns2023503 citations
REFORMS: Reporting Standards for Machine Learning Based Science
arXiv (Cornell University)202319 citations
Against Predictive Optimization: On the Legitimacy of Decision-making Algorithms That Optimize Predictive Accuracy
ACM Journal on Responsible Computing202340 citations